In knowledge-based medical treatment planning, the information of existing plans can be used to make a treatment plan for a new patient, e.g., by estimating an achievable dose distribution. A predication can be made by distilling patient geometry and dose information of multiple previous clinical plans into a prediction model that can be used for dose prediction without storing all information from the original set of plans.
Such a knowledge-based model could have various implementations. For example, it could be a regression model associating geometric parameters to dosimetric parameters. Typically a certain model derived from a training set only has a limited region, e.g., with respect to geometric parameters of a tumor, in which its predictions are valid. If the geometric parameters of the new case differ too much of the geometric parameters spanned by the training set, the dose predictions unfortunately can become unreliable.
A clinic usually has several predictive models that collectively can cover a large variety of different regions. Conventionally, a therapy expert, e.g., an oncologist, has to manually explore the available models and thereby determine one for prediction computation based on a personal judgment. This manual selection process can be time consuming and possibly unreliable, especially when the number of available models is large, and each model corresponds to a complicated geometric parameter set.